{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:31:40Z","timestamp":1763202700785,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T00:00:00Z","timestamp":1664409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Shoulder problems (pain and pathology) are highly prevalent in manual wheelchair users with spinal cord injury. These problems lead to limitations in activities of daily life (ADL), labor- and leisure participation, and increase the health care costs. Shoulder problems are often associated with the long-term reliance on the upper limbs, and the accompanying \u201cshoulder load\u201d. To make an estimation of daily shoulder load, it is crucial to know which ADL are performed and how these are executed in the free-living environment (in terms of magnitude, frequency, and duration). The aim of this study was to develop and validate methodology for the classification of wheelchair related shoulder loading ADL (SL-ADL) from wearable sensor data. Ten able bodied participants equipped with five Shimmer sensors on a wheelchair and upper extremity performed eight relevant SL-ADL. Deep learning networks using bidirectional long short-term memory networks were trained on sensor data (acceleration, gyroscope signals and EMG), using video annotated activities as the target. Overall, the trained algorithm performed well, with an accuracy of 98% and specificity of 99%. When reducing the input for training the network to data from only one sensor, the overall performance decreased to around 80% for all performance measures. The use of only forearm sensor data led to a better performance than the use of the upper arm sensor data. It can be concluded that a generalizable algorithm could be trained by a deep learning network to classify wheelchair related SL-ADL from the wearable sensor data.<\/jats:p>","DOI":"10.3390\/s22197404","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T23:09:29Z","timestamp":1664492969000},"page":"7404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6369-583X","authenticated-orcid":false,"given":"Wiebe H. K.","family":"de Vries","sequence":"first","affiliation":[{"name":"Swiss Paraplegic Research, Guido A. Zachstrasse 4, 6207 Nottwil, Switzerland"}]},{"given":"Sabrina","family":"Amrein","sequence":"additional","affiliation":[{"name":"Swiss Paraplegic Research, Guido A. Zachstrasse 4, 6207 Nottwil, Switzerland"},{"name":"Rehabilitation Engineering Laboratory, H\u00f6nggerberg Campus, ETH Zurich, 8049 Zurich, Switzerland"}]},{"given":"Ursina","family":"Arnet","sequence":"additional","affiliation":[{"name":"Swiss Paraplegic Research, Guido A. Zachstrasse 4, 6207 Nottwil, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4238-0959","authenticated-orcid":false,"given":"Laura","family":"Mayrhuber","sequence":"additional","affiliation":[{"name":"Swiss Paraplegic Research, Guido A. Zachstrasse 4, 6207 Nottwil, Switzerland"}]},{"given":"Cristina","family":"Ehrmann","sequence":"additional","affiliation":[{"name":"Swiss Paraplegic Research, Guido A. Zachstrasse 4, 6207 Nottwil, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0292-6520","authenticated-orcid":false,"given":"H. E. J.","family":"Veeger","sequence":"additional","affiliation":[{"name":"Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Arnet, U., de Vries, W.H., Eriks-Hoogland, I., Wisianowsky, C., van der Woude, L.H.V., Veeger, D., and Berger, M. (2021). MRI evaluation of shoulder pathologies in wheelchair users with spinal cord injury and the relation to shoulder pain. J. Spinal Cord Med., 1\u201314.","DOI":"10.1080\/10790268.2021.1881238"},{"key":"ref_2","unstructured":"Boninger, M. (1999). Repetitive strain injuries in manual wheelchair users. Biomedical Aspects of Manual Wheelchair Propulsion, IOS Press."},{"key":"ref_3","first-page":"8","article-title":"Shoulder imaging abnormalities in individuals with paraplegia","volume":"38","author":"Boninger","year":"2001","journal-title":"J. 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